# Hyperopt

Distributed Asynchronous Hyperparameter Optimization in Python

Repository: https://github.com/hyperopt/hyperopt
Canonical: https://ross.abutalabs.com/products/hyperopt
Homepage: http://hyperopt.github.io/hyperopt
Language: Python
License: NOASSERTION
License Family: other
Topics: hacktoberfest
Last push: 2026-08-24T21:31:40+00:00
Link (homepage): http://hyperopt.github.io/hyperopt

## Health v2 (maintenance only)
Score: 86/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 62, longevity 100
- inputs: {"age_days": 5475, "days_push": 9, "days_rel": 40, "gap_med": null, "n_releases_24m": 1}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 7592, forks 1075 (observed 2026-08-28T04:10:01.855463+00:00)

## What it is
Hyperopt is a Python library for distributed asynchronous hyperparameter optimization over search spaces with real-valued, discrete, and conditional dimensions, using algorithms like TPE. It supports parallel trials via MongoDB or Spark and includes hyperopt-sklearn for tuning scikit-learn estimators.

## Use cases
- tune hyperparameters of a machine learning model
- optimize sklearn estimator parameters automatically
- run distributed hyperparameter search with Spark or MongoDB
- minimize an objective function over a conditional search space
- bayesian optimization of black-box functions
- find best model parameters via TPE

## When to choose
- you need flexible search spaces with conditional and discrete dimensions
- you want to parallelize hyperparameter search across Spark or MongoDB workers
- you prefer a lightweight, mature Python optimization library with TPE

## When to avoid
- you need cutting-edge optimizers like Optuna's samplers or advanced pruning
- you want first-class integration with modern deep learning frameworks
- you need active development and frequent releases

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, benchmarking, data-science
- domain: machine-learning, data-science, developer-tools
- platform: python, cross-platform, cloud
- tags: hyperparameter-optimization, bayesian-optimization, tpe, distributed-optimization, model-selection, scikit-learn, docker

## Member repositories
- hyperopt/hyperopt (main) score 86
- hyperopt/hyperopt-sklearn (plugin) score 30

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:01.855463+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:36:52.873526+00:00, confidence not recorded.
  - readme: https://github.com/hyperopt/hyperopt (fetched 2026-08-28T04:10:01.855463+00:00, sha 69a55ab51e47)
  - homepage: http://hyperopt.github.io/hyperopt (fetched 2026-08-29T08:32:14.425609+00:00, sha 6863268e9577)
  - registry_pypi: https://pypi.org/pypi/hyperopt/json (fetched 2026-08-29T08:32:14.428166+00:00, sha 92d2e2a5d1d8)
  - registry_pypi: https://pypi.org/pypi/hyperopt-sklearn/json (fetched 2026-08-29T08:32:14.430043+00:00, sha 62ec8181c5d4)
- Data as of 2026-08-30T08:39:29.467469+00:00.